This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.
In this course, we will be reviewing two main components:
First, you will be learning about the purpose of Machine Learning and where it applies to the real world.
Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!
By just putting in a few hours a week for the next few weeks, this is what you’ll get.
1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy
2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.
3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.
LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

AJ

This was a very informative course. The videos provided a good background on the concepts and I found the labs especially helpful for learning to implement Python code for each technique covered.

JJ

Dec 06, 2018

Filled StarFilled StarFilled StarFilled StarFilled Star

I am happy to have this online education, I drop out my nuclear engineering degree, I am happy to learn practical things with future... I work for IBM also...but I want to become a data scientis

À partir de la leçon

Introduction to Machine Learning

In this week, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.

Enseigné par

SAEED AGHABOZORGI

Ph.D., Sr. Data Scientist

Transcription

Hello, and welcome. In this video we'll introduce supervised algorithms, versus unsupervised algorithms. So, let's get started. An easy way to begin grasping the concept of supervised learning is by looking directly at the words that make it up. Supervise, means to observe, and direct the execution of a task, project, or activity. Obviously we aren't going to be supervising a person, instead will be supervising a machine learning model that might be able to produce classification regions like we see here. So, how do we supervise a machine learning model? We do this by teaching the model, that is we load the model with knowledge so that we can have it predict future instances. But this leads to the next question which is, how exactly do we teach a model? We teach the model by training it with some data from a labeled dataset. It's important to note that the data is labeled, and what does a labeled dataset look like? Well, it could look something like this. This example is taken from the cancer dataset. As you can see, we have some historical data for patients, and we already know the class of each row. Let's start by introducing some components of this table. The names up here which are called clump thickness, uniformity of cell size, uniformity of cell shape, marginal adhesion and so on are called attributes. The columns are called features which include the data. If you plot this data, and look at a single data point on a plot, it'll have all of these attributes that would make a row on this chart also referred to as an observation. Looking directly at the value of the data, you can have two kinds. The first is numerical. When dealing with machine learning, the most commonly used data is numeric. The second is categorical, that is its non-numeric because it contains characters rather than numbers. In this case, it's categorical because this dataset is made for classification. There are two types of supervised learning techniques. They are; classification, and regression. Classification is the process of predicting a discrete class label, or category. Regression is the process of predicting a continuous value as opposed to predicting a categorical value in classification. Look at this dataset. It is related to CO2 emissions of different cars. It includes; engine size, cylinders, fuel consumption, and CO2 emission of various models of automobiles. Given this dataset, you can use regression to predict the CO2 emission of a new car by using other fields such as engine size, or number of cylinders. Since we know the meaning of supervised learning, what do you think unsupervised learning means? Yes, unsupervised learning is exactly as it sounds. We do not supervise the model, but we let the model work on its own to discover information that may not be visible to the human eye. It means, the unsupervised algorithm trains on the dataset, and draws conclusions on unlabeled data. Generally speaking, unsupervised learning has more difficult algorithms than supervised learning since we know little to no information about the data, or the outcomes that are to be expected. Dimension reduction, density estimation, market basket analysis, and clustering are the most widely used unsupervised machine learning techniques. Dimensionality reduction, and/or feature selection, play a large role in this by reducing redundant features to make the classification easier. Market basket analysis is a modeling technique based upon the theory that if you buy a certain group of items, you're more likely to buy another group of items. Density estimation is a very simple concept that is mostly used to explore the data to find some structure within it. And finally, clustering. Clustering is considered to be one of the most popular unsupervised machine learning techniques used for grouping data points, or objects that are somehow similar. Cluster analysis has many applications in different domains, whether it be a bank's desire to segment his customers based on certain characteristics, or helping an individual to organize in-group his, or her favorite types of music. Generally speaking though, clustering is used mostly for discovering structure, summarization, and anomaly detection. So, to recap, the biggest difference between supervised, and unsupervised learning is that supervised learning deals with labeled data while unsupervised learning deals with unlabeled data. In supervised learning, we have machine learning algorithms for classification, and regression. In unsupervised learning, we have methods such as clustering. In comparison to supervised learning, unsupervised learning has fewer models, and fewer evaluation methods that can be used to ensure that the outcome of the model is accurate. As such, unsupervised learning creates a less controllable environment as the machine is creating outcomes for us. Thanks for watching.